4 papers
What Is a Clarification? Estimating Model-Dependent Input Ambiguity
Jingqin Tang, Tim Tomov, Sebastian Schmidt +2
Ambiguous inputs can admit multiple interpretations, each potentially yielding a different answer. Existing work largely treats this ambiguity as a property of the input, yet for a…
Task-Aware Calibration: Provably Optimal Decoding in LLMs
Tim Tomov, Dominik Fuchsgruber, Rajeev Verma +1
LLM decoding often relies on the model's predictive distribution to generate an output. Consequently, misalignment with respect to the true generating distribution leads to subopti…
Task-Awareness Improves LLM Generations and Uncertainty
Tim Tomov, Dominik Fuchsgruber, Stephan Günnemann
In many applications of LLMs, natural language responses often have an underlying structure such as representing discrete labels, numerical values, or graphs. Yet, existing decodin…
The Illusion of Certainty: Uncertainty Quantification for LLMs Fails under Ambiguity
Tim Tomov, Dominik Fuchsgruber, Tom Wollschläger +1
Accurate uncertainty quantification (UQ) in Large Language Models (LLMs) is critical for trustworthy deployment. While real-world language is inherently ambiguous, reflecting aleat…